Company Overview:
Blink Health is the fastest growing healthcare technology company that builds products to make prescriptions accessible and affordable to everybody. Our two primary products – BlinkRx and Quick Save – remove traditional roadblocks within the current prescription supply chain, resulting in better access to critical medications and improved health outcomes for patients.
BlinkRx is the world’s first pharma-to-patient cloud that offers a digital concierge service for patients who are prescribed branded medications. Patients benefit from transparent low prices, free home delivery, and world-class support on this first-of-its-kind centralized platform. With BlinkRx, never again will a patient show up at the pharmacy only to discover that they can’t afford their medication, their doctor needs to fill out a form for them, or the pharmacy doesn’t have the medication in stock.
We are a highly collaborative team of builders and operators who invent new ways of working in an industry that historically has resisted innovation. Join us!
Job Overview
We are seeking an experienced Senior Application Security Engineer to lead the evolution of application security across Blink. This role will define security strategy, influence engineering architecture, and build scalable security capabilities that enable developers to ship secure software quickly.
You will partner closely with engineering, platform, AI, and product teams to embed security into every stage of the software development lifecycle. In addition to securing traditional web, API, and mobile applications, you will play a key role in enabling Blink’s AI adoption by developing secure patterns for LLM-powered applications, AI agents, model integrations, and developer AI tooling.
The ideal candidate combines deep technical expertise with strong engineering judgment and the ability to influence teams across the organization.
Lead security reviews for AI-powered applications, LLM integrations, AI agents, MCP-based architectures, and retrieval-augmented generation (RAG) systems.Develop secure design patterns for prompt handling, model access, tool permissions, context isolation, and AI data protection.Assess risks including prompt injection, indirect prompt injection, insecure tool use, model abuse, excessive agency, data leakage, and supply chain risks.Partner with AI engineering teams to implement guardrails, model governance, runtime monitoring, and secure AI deployment patterns.Establish secur
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